Images captured with digital cameras using image sensors will pick up noise from a variety of sources such as read noise σr and/or photon shot noise σph. Many further uses of these images require that the noise in the images will be reduced. In order to reduce the noise in the image, noise filtering is used. When applying noise filtering to an image it is important to adapt the noise filtering method and the strength of the noise filtering to how much noise that is present in the image. A Signal to Noise Ratio value (SNR value) may quantify the strength of an image signal in relation to the noise in the image and is hence an important parameter when applying noise filtering to an image. In particular an estimation of the mean SNR value for a whole image may be very useful when applying noise filtering to an image. A further field of use for a mean SNR value is to use the mean SNR value as an indication of when to activate day/night functionality in a camera. A small, or a decreasing, mean SNR value indicating that the image noise is large, or increasing, in relation to the image signal may, for example, indicate that the night functionality of the camera needs to be activated. A further field of use for a mean SNR value is to use the mean SNR value in auto-exposure algorithms for finding an exposure time generating an acceptable noise level.
The SNR value for a stochastic variable S is defined (definition used for imaging) as:
                    SNR        =                  20          ⁢                      (                                          log                (                                  E                  ⁡                                      [                    S                    ]                                                                              σ                ⁡                                  [                  S                  ]                                                      )                                              Equation        ⁢                                  ⁢                  (          A          )                    
where E[S] is the expectancy value of the signal, S, and σ is the standard deviation of the signal S. In an image processing pipeline, it is very costly in terms of processing capacity to calculate the SNR value from image data using Equation (A) and an estimated SNR value is hence needed. For cameras using a single exposure to generate an output image, this is often done by using the average luminance value in the scene as the expectancy value of the signal and using pre-calculate read noise values for the standard deviation of the signal. However, this approach may not work for multi-exposure images, such as multi-exposure HDR images. This is because it consists of images stitched from different exposures, with different exposure times, which for example implies that noise levels and/or sources may vary between different exposure regions of the stitched image, which also implies that the SNR value is different in the different exposure regions of the stitched image.
Accordingly there is a need for an alternative method for estimating a mean SNR value of an image.